Learning rich, interpretable representations with deep neural networks is one of the main challenges of current artificial intelligence research. Achieving such representations would enable us to perform complex and highly useful operations on high-dimensional data [1]}. Perhaps the first milestone that has yet to be reached in this research is learning representations which easily factorize along the lines of recognizable human concepts. This property is called “the disentanglement of generative factors”, and is an accelerating field of inquiry [2]}, with many major contributions coming from models based on the Variational Autoencoder [3]}, [4]}.
